This invention relates generally to methods and system for filtering and binarizing images, and, more particularly to binarizing images in the presence of specular noise, where the information of interest in the image is contained in edges.
Edges contain a significant portion of the information in an image. In some images, such as images containing addresses (text) and bar codes typical of address labels in postal items and packages, edges contain almost all the information. Recovering that information in the presence of specular noise can be difficult. An example of an application where those conditions occur is the recovery of address and bar code information in mail pieces with high gloss wrappings. The specular reflection caused by the high gloss wrappings introduces noise in the image and renders the detection difficult. Typically, OCR systems are used to recover the information. Binarizing in OCR systems, as described by Wu and Manmatha (V. Wu, R. Manmatha, Document Image Clean Up and Binarization, available at http://citeseer.nj.nec.com/43792.html) is traditionally performed with a multi directional global threshold method. Under such a binarization method, the results obtained when the image is obscured by specular noise can be difficult to interpret.
Many difficulties are encountered in recovering the information from images in the presence of specular noise, such as the noise caused by transmission through high gloss wrappings, when global binarization methods are used.
Several adaptive binarization methods have been proposed (see, for example, Ø. D. Trier and T. Taxt, Evaluation of binarization methods for document images, available at http://citeseer.nj.nec.com/trier95evaluation.html, also a short version published in IEEE Transaction on Pattern Analysis and Machine Intelligence, 17, pp. 312–315, 1995). Such proposed adaptive binarization algorithms are in general complex, difficult to implement, and, therefore, have not seen widespread use.